Sparse Representation Convolutional Autoencoder for Feature Learning of Vibration Signals and its Applications in Machinery Fault Diagnosis

Vibration signals are widely utilized in many fields, which can reflect machine health state. Those typical deep learning techniques cannot learn impulsive features from vibration signals due to interference of strong background noise. Supervised learning greatly rely on vast labeled data, which lim...

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Vydané v:	IEEE transactions on industrial electronics (1982) Ročník 69; číslo 12; s. 13565 - 13575
Hlavní autori:	Miao, Mengqi, Sun, Yuanhang, Yu, Jianbo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Background noise Convolution Convolutional autoencoder Deep learning Fault diagnosis Feature extraction Filtering Filtration Industrial applications Machine learning Machinery machinery fault diagnosis Representations Rotary machines selective residual learning sparse representation (SR) Training Vibration Vibrations
ISSN:	0278-0046, 1557-9948
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